Head and neck squamous cell carcinoma (HNSCC) is a major health problem affecting current and former tobacco users, with approximately 40,000 cases per year in the United States and 500,000 cases worldwide. The development of HNSCC is heralded by the development of dysplastic lesions within the mucosa and early diagnosis of pre-malignant lesions is known to directly correlate with increased survival. However, current diagnostic techniques for premalignant disease, based on recognition of nuclear and cellular atypia, are relatively poor predictors of ultimate clinical outcome. Since morphological or cytological changes may occur late in the process of transformation, histologically benign appearing lesions may have malignant potential. Conversely, even severely dysplastic oral lesions can undergo spontaneous regression. Thus, histological findings cannot clearly predict malignant change. A method for detecting molecularly premalignant lesions at an early stage and for predicting the likelihood of malignant progression is required. [unreadable] [unreadable] The goal of this proposal is to identify a panel of biomarkers that will allow for the identification of molecularly premalignant lesions. Accordingly, gene expression profiling of microdissected keratinocytes from biopsies containing normal mucosal tissue from nonsmokers and invasive HNSCC will be performed in order to identify potential oral mucosa progression markers (OPM). The interpretation and analysis of the array data will be performed within a newly established Bioinformatics Core. This web-accessible annotation, cataloging facility will use both unsupervised and supervised learning methodologies. The utility of these potential OPMs will then be validated using custom human tissue array samples having the different histologic diagnoses including: normal, reactive, hyperkeratosis, hyperplasia and various grades of dysplasia. These analyses may identify markers that correlate with the biologic severity of dysplasia and with the potential for malignant progression. This molecular analysis may also greatly improve diagnostic prediction compared to the cellular pattern recognition currently used by pathologists. [unreadable] [unreadable]